{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "74f367db",
   "metadata": {},
   "source": [
    "# DAY8 - 标签编码与连续变量处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42814709",
   "metadata": {},
   "source": [
    "## 1.1 字典的简单介绍\n",
    "\n",
    "可以去b站找个视频 或者 csdn找个帖子看下字典的简单介绍----锻炼下自学能力\n",
    "\n",
    "但是目前我们只会用到映射这个用法,他需要传入的是字典,因为字典的键值对,键是唯一的,值可以重复。这很符合数据的特征是固定的,但是值可以变化这个特性。\n",
    "\n",
    "所以后续想完成新的映射,直接修改字典的键值对即可。\n",
    "\n",
    "**提示**: 字典是Python中非常重要的数据结构,在数据处理中经常用于映射和转换。\n",
    "\n",
    "**字典的性质**:\n",
    "- **可变类型**: 字典创建后可以修改、添加或删除键值对\n",
    "- **键的唯一性**: 字典中的键必须是唯一的,重复的键会被覆盖\n",
    "- **键的不可变性**: 键必须是不可变类型(如字符串、数字、元组),不能用列表做键\n",
    "- **值的任意性**: 值可以是任何数据类型,可以重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e268695e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Alice', 'age': 25, 'city': 'New York'}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用花括号创建字典\n",
    "my_dict = {'name': 'Alice', 'age': 25, 'city': 'New York'}\n",
    "\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "868d91f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Alice'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 访问字典中的值(查)\n",
    "my_dict['name']  # 访问字典中的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2044fc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Alice', 'age': 26, 'city': 'New York'}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 修改字典中的值(改)\n",
    "my_dict['age'] = 26  # 修改年龄\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2f3bc2e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Alice', 'age': 26, 'city': 'New York', 'job': 'Engineer'}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加新的键值对(增)\n",
    "my_dict['job'] = 'Engineer'  # 添加新键值对\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "238d4da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Alice', 'age': 26, 'job': 'Engineer'}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除键值对(删)\n",
    "del my_dict['city']  # 删除city这个键值对\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d271d42c",
   "metadata": {},
   "source": [
    "## 1.2 标签编码\n",
    "\n",
    "之前介绍了离散数据 如果是不存在顺序,则采用独热编码,函数为pd.get_dummies()\n",
    "\n",
    "现在介绍对于存在顺序和大小关系的离散特征,做好标签编码,借助dataframe的map函数即可实现\n",
    "\n",
    "**关键点**: 标签编码适用于有序分类变量,可以保留变量之间的大小关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2c23374c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入库并读取数据\n",
    "import pandas as pd\n",
    "data = pd.read_csv('E:\\study\\PythonStudy\\python60-days-challenge-master\\data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d05fa51d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Years in current job</th>\n",
       "      <th>Tax Liens</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Months since last delinquent</th>\n",
       "      <th>Bankruptcies</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Term</th>\n",
       "      <th>Current Loan Amount</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Credit Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>482087.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>685960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>47386.0</td>\n",
       "      <td>7914.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>1025487.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>1181730.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Long Term</td>\n",
       "      <td>264968.0</td>\n",
       "      <td>394972.0</td>\n",
       "      <td>18373.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>751412.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1182434.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>308389.0</td>\n",
       "      <td>13651.0</td>\n",
       "      <td>742.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>805068.0</td>\n",
       "      <td>6 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>147400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>121396.0</td>\n",
       "      <td>95855.0</td>\n",
       "      <td>11338.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Rent</td>\n",
       "      <td>776264.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>385836.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>125840.0</td>\n",
       "      <td>93309.0</td>\n",
       "      <td>7180.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id Home Ownership  Annual Income Years in current job  Tax Liens  \\\n",
       "0   0       Own Home       482087.0                  NaN        0.0   \n",
       "1   1       Own Home      1025487.0            10+ years        0.0   \n",
       "2   2  Home Mortgage       751412.0              8 years        0.0   \n",
       "3   3       Own Home       805068.0              6 years        0.0   \n",
       "4   4           Rent       776264.0              8 years        0.0   \n",
       "\n",
       "   Number of Open Accounts  Years of Credit History  Maximum Open Credit  \\\n",
       "0                     11.0                     26.3             685960.0   \n",
       "1                     15.0                     15.3            1181730.0   \n",
       "2                     11.0                     35.0            1182434.0   \n",
       "3                      8.0                     22.5             147400.0   \n",
       "4                     13.0                     13.6             385836.0   \n",
       "\n",
       "   Number of Credit Problems  Months since last delinquent  Bankruptcies  \\\n",
       "0                        1.0                           NaN           1.0   \n",
       "1                        0.0                           NaN           0.0   \n",
       "2                        0.0                           NaN           0.0   \n",
       "3                        1.0                           NaN           1.0   \n",
       "4                        1.0                           NaN           0.0   \n",
       "\n",
       "              Purpose        Term  Current Loan Amount  \\\n",
       "0  debt consolidation  Short Term           99999999.0   \n",
       "1  debt consolidation   Long Term             264968.0   \n",
       "2  debt consolidation  Short Term           99999999.0   \n",
       "3  debt consolidation  Short Term             121396.0   \n",
       "4  debt consolidation  Short Term             125840.0   \n",
       "\n",
       "   Current Credit Balance  Monthly Debt  Credit Score  Credit Default  \n",
       "0                 47386.0        7914.0         749.0               0  \n",
       "1                394972.0       18373.0         737.0               1  \n",
       "2                308389.0       13651.0         742.0               0  \n",
       "3                 95855.0       11338.0         694.0               0  \n",
       "4                 93309.0        7180.0         719.0               0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据前几行\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a0e26c",
   "metadata": {},
   "source": [
    "### 1.2.1 Home Ownership特征编码\n",
    "\n",
    "这里我们给Home Ownership来完成标签编码,实际上这个特征也可以独热编码,取决于你的理解。实际中,都试一下,谁训练出来的模型好选谁。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "940958e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Home Ownership\n",
       "Home Mortgage    3637\n",
       "Rent             3204\n",
       "Own Home          647\n",
       "Have Mortgage      12\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看Home Ownership特征的分布\n",
    "data[\"Home Ownership\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22826de",
   "metadata": {},
   "source": [
    "**数据含义解读**:\n",
    "- 住房抵押贷款(Home Mortgage): 3637   这个是有房贷,有房子\n",
    "- 租房(Rent): 3204     没房子\n",
    "- 拥有自有住房(Own Home): 647    这个没贷款,有房子,没房贷\n",
    "- 有贷款(Have Mortgage): 12   这个是有其他贷款,有房子,没房贷\n",
    "\n",
    "上面这些翻译也不一定对,这是我个人对数据的理解。\n",
    "\n",
    "按照贷款严重程度(抗风险能力),依次是: **自有住房 < 租房 < 有其他贷款 < 住房抵押贷款**\n",
    "\n",
    "所以按照这个逻辑来进行编码,数字越大表示负债压力越大。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f20dcdf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Own Home\n",
       "1         Own Home\n",
       "2    Home Mortgage\n",
       "3         Own Home\n",
       "4             Rent\n",
       "Name: Home Ownership, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义映射字典\n",
    "mapping = {\n",
    "        \"Own Home\": 0,\n",
    "        \"Rent\": 1,\n",
    "        \"Have Mortgage \": 2,\n",
    "        \"Home Mortgage\": 3\n",
    "    \n",
    "}\n",
    "data[\"Home Ownership\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ac81e3ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    0.0\n",
       "2    3.0\n",
       "3    0.0\n",
       "4    1.0\n",
       "Name: Home Ownership, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 应用映射并查看结果\n",
    "data[\"Home Ownership\"] = data[\"Home Ownership\"].map(mapping)\n",
    "data[\"Home Ownership\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ea3d2779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Term\n",
       "Short Term    5556\n",
       "Long Term     1944\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看Term特征的分布\n",
    "data[\"Term\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce470c8",
   "metadata": {},
   "source": [
    "### 1.2.2 Term特征编码\n",
    "\n",
    "对于字符串类型我们也要映射成整数类型,这里不要理解为标签编码或者独热编码\n",
    "\n",
    "我们在复试班说过,**二分类的问题不需要独热编码**,比如性别这个特征,男女不需要变成2个特征(性别男、性别女)。因为他们二者自由度为1,如果是2个特征的话,性别男=1,那么性别女必定等于0。这样特征高度相关,没有价值。\n",
    "\n",
    "此时这个特征的含义不是性别,而是:**是否为男性**,1是男性,0是非男。\n",
    "\n",
    "**记住**: 三分类以上才涉及独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "20b8666a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Term, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对Term特征进行映射\n",
    "# 定义映射字典\n",
    "mapping = {\n",
    "    \"Short Term\": 1,\n",
    "    \"Long Term\": 0\n",
    "}\n",
    "\n",
    "# 进行映射\n",
    "data[\"Term\"] = data[\"Term\"].map(mapping)\n",
    "data[\"Term\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c36696e",
   "metadata": {},
   "source": [
    "### 1.2.3 使用嵌套字典批量映射\n",
    "\n",
    "实际上借助一个映射函数也可以实现上面2次编码\n",
    "\n",
    "字典的键值对可以嵌套字典,这样可以把多个特征的映射规则集中管理,代码更简洁。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dc2d6524",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建嵌套映射字典\n",
    "import pandas as pd\n",
    "\n",
    "# 重新读取数据\n",
    "data = pd.read_csv(\"E:\\study\\PythonStudy\\python60-days-challenge-master\\data.csv\")\n",
    "# 嵌套映射字典\n",
    "mapping = {\n",
    "    \"Term\": {\n",
    "        \"Short Term\": 1,\n",
    "        \"Long Term\": 0\n",
    "    },\n",
    "    \"Home Ownership\": {\n",
    "        \"Rent\": 0,\n",
    "        \"Own Home\": 1,\n",
    "        \"Have Mortgage  \": 2,\n",
    "        \"Home Mortgage\": 3\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e957c9f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Short Term': 1, 'Long Term': 0}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 访问嵌套字典\n",
    "mapping[\"Term\"] # 访问嵌套字典中的值,此时他又是一个字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1feac37a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Years in current job</th>\n",
       "      <th>Tax Liens</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Months since last delinquent</th>\n",
       "      <th>Bankruptcies</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Term</th>\n",
       "      <th>Current Loan Amount</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Credit Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>482087.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>685960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>1</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>47386.0</td>\n",
       "      <td>7914.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1025487.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>1181730.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>0</td>\n",
       "      <td>264968.0</td>\n",
       "      <td>394972.0</td>\n",
       "      <td>18373.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>751412.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1182434.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>1</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>308389.0</td>\n",
       "      <td>13651.0</td>\n",
       "      <td>742.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>805068.0</td>\n",
       "      <td>6 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>147400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>1</td>\n",
       "      <td>121396.0</td>\n",
       "      <td>95855.0</td>\n",
       "      <td>11338.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>776264.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>385836.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>debt consolidation</td>\n",
       "      <td>1</td>\n",
       "      <td>125840.0</td>\n",
       "      <td>93309.0</td>\n",
       "      <td>7180.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Home Ownership  Annual Income Years in current job  Tax Liens  \\\n",
       "0   0             1.0       482087.0                  NaN        0.0   \n",
       "1   1             1.0      1025487.0            10+ years        0.0   \n",
       "2   2             3.0       751412.0              8 years        0.0   \n",
       "3   3             1.0       805068.0              6 years        0.0   \n",
       "4   4             0.0       776264.0              8 years        0.0   \n",
       "\n",
       "   Number of Open Accounts  Years of Credit History  Maximum Open Credit  \\\n",
       "0                     11.0                     26.3             685960.0   \n",
       "1                     15.0                     15.3            1181730.0   \n",
       "2                     11.0                     35.0            1182434.0   \n",
       "3                      8.0                     22.5             147400.0   \n",
       "4                     13.0                     13.6             385836.0   \n",
       "\n",
       "   Number of Credit Problems  Months since last delinquent  Bankruptcies  \\\n",
       "0                        1.0                           NaN           1.0   \n",
       "1                        0.0                           NaN           0.0   \n",
       "2                        0.0                           NaN           0.0   \n",
       "3                        1.0                           NaN           1.0   \n",
       "4                        1.0                           NaN           0.0   \n",
       "\n",
       "              Purpose  Term  Current Loan Amount  Current Credit Balance  \\\n",
       "0  debt consolidation     1           99999999.0                 47386.0   \n",
       "1  debt consolidation     0             264968.0                394972.0   \n",
       "2  debt consolidation     1           99999999.0                308389.0   \n",
       "3  debt consolidation     1             121396.0                 95855.0   \n",
       "4  debt consolidation     1             125840.0                 93309.0   \n",
       "\n",
       "   Monthly Debt  Credit Score  Credit Default  \n",
       "0        7914.0         749.0               0  \n",
       "1       18373.0         737.0               1  \n",
       "2       13651.0         742.0               0  \n",
       "3       11338.0         694.0               0  \n",
       "4        7180.0         719.0               0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 批量应用映射\n",
    "# 对 Home Ownership 列进行映射\n",
    "data[\"Home Ownership\"] = data[\"Home Ownership\"].map(mapping[\"Home Ownership\"])\n",
    "\n",
    "# 对 Term 列进行映射\n",
    "data[\"Term\"] = data[\"Term\"].map(mapping[\"Term\"])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1a5383",
   "metadata": {},
   "source": [
    "## 1.3 连续变量的处理\n",
    "\n",
    "归一化和标准化可以通过手写函数实现,也可以使用sklearn中的归一化和标准化函数。\n",
    "\n",
    "**为什么需要处理连续变量?** 不同特征的量纲和数值范围差异很大,会影响模型训练效果,需要进行归一化或标准化处理。"
   ]
  },
  {
   "attachments": {
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YoeTX+SpafrNujh2+7owP1T5fFD/fGoo+Z80fjdkA/BOtkcbSc0rV+p+HdfCR0vgeAVN4+78SGnu/ZHbKhugP/KhCdS9ZI2FTtPhXeyOl4RJM+Uanfr0koWhZd4WKHjg5JajOn3NV/DrduTe+euif39bzu+t01bTPaHbVZnkItwAAAAAAyCoTKtTShU6V1qzVzbnWeDKvP6R1buu24eybvqai2FJFSZ0dLs015G1t3R4NYwa0Y2tcXKKvpax6aLpSqzZu0qbY4d4KxVWW/OdV8fOt4ebLrPnp+KdV6vG8osN//rVWXZ20nmJSA39+27plufT8+GWzvN9doatujw/AZt77e3WWpXqtKSp9pFM3J8z23P5l3RkXjGXm6PsDGnh9r57+xcO6s2qZvjxnuqaXPKy3L7tKVnP6EUd36Ncxr3NgMLw7Ks9jy3TzEwnvGwAAAAAAnNYmVqiVs0Cbm0YqpSXtfWSdhuKL87Xqu3HRR2pzKxQXaz3wcKTkj3+HOmNCMrM9rwUns+phBnIuc2rKqIFdrKN6+0/xbWjlfH54pPW++04VfSW+6uCU+Z3ae1tima6ERvH/MltrH785oXH7t9V8ber2tY4GzMce0N5fPK2n29epM6Enx6dvnK7plxZrwcIVan5ss3peMu7/nvEe/u5KFV1i3SlsQOs6hsrTJVZHnP1PyaI7AAAAAABwuppYoZamhBslT+n9rVrXHhPauFZpRVzwMYJPl+rm6piw6uhmdf5WOrBxXXy1thEbnTeNvffDgcAojbZn5ID2vpiQJL3/tDq7rduW2ZfGB1Xv716h2Vc2K6GSot7fWaHp/8+n9KlPxQ6f0dTEBu+/snl4O1rGdqn4Xk/c9LcfyA8/x2emmY/LV/HCBVrw3YflSXsVnK+isvhWxI62N2uz37z1vPbuDE+yFOnK+Ca4AAAAAADAaW6ChVqjOLtUrT2tWnxZJJz6WtUCKSY0OvDYw3ENyCcq+vrNkWqFn85R0fcf0s2XHlBnQrW1r319pKqHprH3fjj9Sw/HlDLL0OfPj7SxFaNn8VRNnfNlLatapoqifE2dVpHQKHyOnBfFvpv31dN+Asswgvef+LIqnhyKtT4zZbp1a+yci29OqDq5V8u+tEzrVt+pzbHhWM6Vmp1+K/oAAAAAAOA0cIaFWlM0c+4qdXr+V//7plcP/3OnSmNCo/yqHo1YEOifb9Gve7z63/99T3sevFnOv+zV3nDJn6jFuvnrI0dan5hPX6WrXNbtGO+/1KPNj23W1t8e0PsfWxOjzl6gorh2vKZo8fdvGSW0G7uexQusklRSTk6aKdOnpygnd6aKvnGzVt3fqR09r+jgb2+JhFmXrNCqxPf81mbV3bc3bjuf/fUvDwv8AAAAAADA6e3MCrVinH3hTOVceJWuGimh+VKRZls3wz49U0VzZ+rsaDtVn79Fv//woHYst6roLalQaRptWE3JzVHOWIbPnUiclKObGxPbsxrJFC3evnZ4I/pfulOtScKx5M6OX/7LS+N7dLw2sR0rr3p+a5UDmxnf66K5zpxlxmPWPKQd2/fI+957Ovy/x3X8/3dY7/3Zqz1bN6n1tsX62lynzndE11OObvnp2pS9N0ZM0S2LR26HDQAAAAAAnH4+ddxg3Z5Yuiv0qa9stUZmqvVNr1ZdaI0OGtDmoula9ltrNM75WvWfXrVenV6QdPT1h9X5/i26+WprwqAeVXzqy4ouiVma69fHO1VqjZ2wt9Yp/6K6oeqLl7Tq4GurEqrdDRnYuUxFCzfrQGKprFg5V6qho0dr5yaPwI7uXKEv/+Jslc69Sk7nVcp3WDPMUlODgVIaPvbozotmq/lPxu0Lb9aO327S16I9V/5lQAcC0nRHjqaM2EbZ6N433vPMr202tvZwU5b8Wm8/XppB2AcAAAAAAE4HEzfUeq5O0ys7rZHZuvM/f61bPm+Nxnj7yTo1/za+6fIc52JVLCnSzBMMUyL2qu5zFYouiWTc/nOr0uxzcXQDPWpufHqonavPfU1r15RqxMp7Hx/V+/4D+v2Lb8dXwzvfqdmfn55ZMHWCju5eoYrnK7S58coMe2rMzNGBvcZrNOvh3V6Fm+X/u3x9+btr1Vp9cl8XAAAAAACcHBM31AIAAAAAAMCEdca2qQUAAAAAAIDsRagFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoBQAAAAAAgKxDqAUAAAAAAICsQ6gFAAAAAACArEOoNYLA673qfbZX7oMha8onL/hqh1q2+6yxURzzqXtTr3xHrPHTTkBeY/32Pus1bo3M+1ynevcZ90t3Uwx4w9uu9/XRnvlk8qtzeYEKCgvU0m9NQhpC8vWb+4U74303dNCd9jHr3d6ghjXt6vvEdhG/cXx2yTsQu6xBeTbVqWVv0BrPVFC+F4zjJOl6CykYCCgQCCp0zJp0uhroU+eTxvYPjrIdj3jUtalb7jfH8p586ry9RV3GOSKd00pkf2lQ1wFrAlILhdJap3ECfWo31m/DGuOYsCaly//E0vB5dv4D7lFf1/foorTve9p6t1NVxnsoKGyR25qkY8bnqXEcpMVY111PGufYF/zZuw4AAAAshFoj+MPjNaq5tUZNew9ZUz5hwW7V3dCijjWlWvSz0YMt36Ya1T1Qo9LLa9Q91mvkQUF1V8/QjBnGUNRuXA6OQcif8KX7D+ow1m/NrR3GrSH+XR3qHbBGwrzqvbtJNcvr9Ku46SPwdoS3Xc3jsc986oXeCygwENAhrhwycEh995n7RZP6Mjz0Du019pM0j9lDL3Wpa3u3fH+1Jown46J+VC90qOGBBs2/tWsw1A3ta9HSB7rVUV2jrnT39VgD3Wq4cb4KnLPU9LI1bZBbTQXGhXDBInX6rUknUyiogBmivetVnxkwP9kWDoWqFhjLMLdFnhFCqMBz7Wq6e6lKlw+tm2RCe811WKel3+qQb5I1MV0vdKrlGePx3/u5fGkEYpH9pUvu96wJoznikzsc2o/HkHnA+4k53KuGolla9Kgvs8Dkrz51G+u3a7vbOANkwPhcbFrrNs6zF6piiUs2a3JSR7rV9qDHuO/nVD7afUcRfNObfJsY271vU51qjPd/0nwckt/4XAkMHLLWsV+dSwo0f94cLVrvMT6tR7HfOE/cbZxj7+42HgkAAJDdPnXcYN1Ggr7bZ6jqGSnvtj3qWZZrTY1yq6VwlX5ljY2Pr2hdX71c1lgywb11Kq7uDn9pdd7Ro23fyovMSPR6m4oXtBtfWO0qa9+j1iK7NWNsgs/V6eoV3dYXaJvKHvqdWq/N5DmNC+pZS9UZylf97p2qvMCc1qe6GVXqVpk2vtGqQnPSwQ7NNy54vZNK1OreoDLzJQJdWlrQILetQtv2N8pp3m80xnqaYawnXb9Rb9wffmaDXx3zitXypjWakTzV9/ao8jxrNC1Dr1fW/oaxDazJGEV0vWW+zv2bSlX8gC/FMRsvcnwneY3ovpORoecJ7mvS/OXdco543IXUvWKW6p6TStbt14Z50cvroLFcxcZyGUf4JfXq2V6pvAzCmuj717nV6tlbayxVrOjxNpZ92Sw91yffh9FRvzwv+QYvnkPvePT8Ox+Eb390KKBgGiFR6m00dNzEr5tEQ+sw97s92nNrinNhUgF13VighhekwnUvauO80c9l0c+DtI/ldzpUWtIyth8AhhnLNvtkxH5G5d/Wo53L0twug+sr5vNgVOaPLXNUt9e4eU2tNixI8VqT81RYkCfv2jla9IR5bFWqdbkzdaiVO1sllziskWR8aisqVfu7kuuefm1ZGHPfF5o068ZOhYzPqy2eRrkyDVvTkWRdBV9tU9Xi9nBYbC9o1rbHylOeO/w/m6/i+7wJn48nLrCvXT967mTGZLkqubVahSNtGgAAcMaZWKGWWSR/8SNxpX6Suvi7euqxCuPr0chGDrWiF4jjKb0v80MXDXmq3d2j6nBAFONInxoKq9RlfHfP6KIilWjQZNzMvSBX/oPGl1ZbmTb8rlUlGeRag8ttN97nHuN92hNCraAxXmyMBxOCuKQB1SiSPiZ6sZwr10LXqNs/yt/fJfe7Y7moHLo4zy0ol+tca/JoLq1Q88J8a+TMYFb1fWXwWuiQeu9pUnfAobI771LJNGtyGg499wM17QrIMa9Rd1079MDcy0qUn2ONWFKGWv0tKlidaVz9BX1360ZVGNvYt2m+Sh8wj5bYADfBm20qntcu/7nV2vlcrfJjLz6jx8EFtdr502rln2NNH9XQhbbzzn5tW5J45ZdOqBVS6IhNtmGvObQvZ2SSXY5pZxk3PqsvXJOvabIr73Kncm02Y5u4jG2SJFaIrpvRQoEj3apx1qnXOJKrd+1R7UXW9HREQ/+k4V9yYw61HGVqXFNivPfhUu2vQ6LHQvaEWibfzxap9D5P+PaIP77EGkOoNXSsjeKievX8wK9FizsHg9gRjfJZE9xVpTmr+sKfgxvdxrLGHS8jBF7j5Viv6vJr4n8QMh3s0tIbGuQ236TxHvYb7yFZcNe32tiXdxhnqDv2aOe3Un0SetW1plORrTgSpyruKTfOdsZZIhqqnzTZdRwAAIBTY2KFWun+Mm58wd2zq/IEQy2zfZoP9ZE1lpL/57rxBrO6Xola++/Sldbk5M7SZIc94Uto8i+Wvr1d8tgLVX758Auh0B971f2q8a12Up5KFjiNy8hYQ19A0xLyqX1xqdpeN26HS46UqG+hcXE7OJ5JSZKQ8WX6auPLdNAK2/wxoVajgsvnqG7f8CDOffcsLX0ylFlppxFDrfQvmkwpw49RjTEIyCS8myCix9rJkmzfGft2HY2x3RdYx8hggBuZEzFUwij/1p16bOFnrelDPhj4H9lyPquzrfE4Z02Ww57kUjVaQsQ813g2qGxYMDV6qBUOCR4MqfIn21R/TexCR/dlm/KvK1O+Mct+nkvO3Ohy2JR76Rf02fBosvPYKMyqevsiJb98z9SpbW8oackb+8WFcl0QeebAE4tUsNY4M04tUf3dZSOcz3M1+7p8DUUL0dI9NmO/eNHYL4aW1Gw3q/M1ayRBJNweOaDOvfb7qr7GeqXo59EInzejlyyMrvfsu5gfCpzsKntoz+glezMMtQK7ajR3Ve/QjyQfdqvqq3XyXb9FO+90xX/uJfnBxP9Mlebf7pFr3W5tmJdB8BTzo1Gq7RYylm2WsWzJQ6/xED2Wk6wrs/rn7V6Vr6uVM+kqHzo/lT/2hpqvsSYPE32N0QwtQ7oltYKvdav3gHGM2/JVcn1+wneUkVBSCwAADDexQq1jIQUPjxA0+Tbrhhs75B+XUCtNY/j1OV66XyzTlcFyHPOpY2Hp8IvzwQsEY/L1G7TnvhLZ0w22jOf0vDpNzi+aT5TwxTzkU9f2QypZEnNBcsytJqdZbXHoYjqp6SVaeolXW6JfqN91q6vfuH2uS+UFkW3nXFyt4G3mBWJm22I8Qq2S+/t1V4E1eTSpQosJLD5MCMr7TK+8o23zJKIXS7aZJSq7dOiBzsXN+pdD8RdckZAi/jXiQokTEXOMDAt/B6sGj1HS0HOoOl3qqnjR4y35vhxbbcy+YKN+d29sKY+THK6k+YPE0Ll4qDTM6BKO95ebNMcssXNFs/ofL4+EXWYbaDbbCYercZ8VZ3ioZR7Hkaq0dlU8vlONV4xyIGfwWenbvlSL1rgVtLvU/NQWlYdLQw5V3c1f2aNt386z9t+gulfMCYfIcfu1+fm22Ph8ezWD0mQGT7QKY7JSloOGzv1243jdYxyvSd99uqXLh/lIgQHzSDWeP8chsyxkShd/V0/ckae3/hi5v/EO1HFrh/F/ipKwg1Uvo+cLpyrXVyap9h99nsw+T02DJbrS/C4GAAAwkjOrTa1o6Z15G/TGOtuYwqKMA65xC7VGevx43SeG8YW/a/kiNfSbX4STVKOKqZI4erAVUN8jP1LvsAaW/XJvdxv/J6kOOL1E3/9uoSZHf/EejfHl+Odf7dI3R6j6YP5Cn/+geaGRTqm5Ic/fU6C6Z08s1KJNrUyM/UJ+pJAgnaoxGR/fI4kJr3K/vVN7VprlI4fCJ6WoljZqlbRk7f1EgxrjyG59cWOkLbphRgi1Yo7n5CUwT1GolaLkRjSsHNw+g+0WFar2/vLk1QcP9eoHa7sVuKBWPburrftE30euqnfsUe0lQ9PaPl2uxrXV+pecpOXjrPPAyAH1Wec4ZI+Wyhk899vkyJlsTUxwOBDp0dXmkGNqZFKiDwfM3hmzMdQyHPHJF8xTXkLV36TS/ayMlpRSJNAqcwT0YfSXrI/f0ubFDQresVHfd/69NdHw8Z/1ix/3yfm9b+rCT1vTTOb9v9Mm+90P6esXjP5jQmhfg65e3mUcZ3ZVbH1RjV+0ZiRzsF2lc9vC2z9lG5SD7/kkSuOzMc5gaD7ad4YMv1PEINQCAADjacL1fhh6p0s1X21QX/RHyRj+g5G2N/IuTu8X2TNW0KO2haVWoGVW19gyvF2gCyq1pb0sfPEZfKbGuKDukC/SinwSR+XrifQcFj+YgZbJDLcS5vX4jEcF1PWEGWjZVPH4G3rjjdhhj+rDbeiY7YoZ48YX4zmLt6m/vz8y3F9izpSuax2c1jh4IdqrunAvcOkN5oUssl9u7P7Rv1EV4RAhT9VPRaf1a9vicby8uqRWG28zg6x8lRVFnje4a3Uk0DJNzVfhdSUqSRgKZ0Yuxv9+ZuGweeFhWAPWxnGy3mor6CJXiipHIwi3w2MFWmaJzJ9m1jj9uPrHMtXf06zmhKF+Xux2Md7vw2Y1S8l5272qTraOzOGafIXX5CSbVWLHeOT2hnDIbF/QqOpwoGVuk6bwtNC70rQLcuVwOJIO0bzDNjn5fHMYDLQQcU6agVYmzilUc2+PenojJbTca2PO14VL1fGuX10rSuPO4QWFi9S2o11LC2Omhacb9z9gfN7dYNxe67ZeIIVgnxpWmYGWuf+0qn6kQMt0QbU2hI9/s7rxfDW9kORLSW6Ftg2ekzIZtlltacafv5IOT1To/IvLVL6wPDyUzLR2ZLMUszUtPFglmrNTSIHX++R+J+WXEAAAMIFNuFDLZ3xx7T3Qpbq1fZGLvBjeA5FfKp0zzVCrUK1xIcnwYeP14buHSwdEp41bKY7TVDgULF6kdrPKoRwqH6EHN3tRq/Y8ZFXfebVFpUVV6nwz2ZfKXFXuSli/fbGNzJdpg2e/9sfON3+9ffkRtb1q3SX21/WwoIKHzb/GBWv0ivUc+9AF5uToFejQtKEf4c3qFBu0Ic2hMuXFi1+dy80Lo1TDDWozLpZN3fXJ5scPLf2R+2Ys5Jf7yTbjgqtOdWta1LHPH77gDzvsUed9daqqrlPLJrcCafRKd/rwqW1x8nWVarjhxyOURojdP/7qkzu8/xj78VRrmrmPjHMokbdsi15071TtZcbOHuxWndm49DgL7f2hccFsjWQqtmHppO1/JfKppWSGZswYvyHcc10mjPNCi/l+bRWqXZwY8MU4lnAuMtb/6jVmcFGoxn+LVkHzqO0H5jaxqeze+pPQ9pHholo91dev/iTDU9+L/MCS972nks7v73sqswbwUwrJv69DLeY54r5Oeax9Pyr4Qovmr4pUPc0KU/OUF1eyzSx9a6yvsQxPRUvxjcCsqnhTVaQ68bnV2nJP8gbYE5nH/8brzQPK+Ky4canxeZawhifZZI+ekzIapg2WjLbZk82PGaba5LimejAgLs+PHBeu76yLC46/f13ku40jJ1mHBSdR0CfvCYZRnvuuVsGCKi0tuVpNL1sTAQDAGWPChVr5t25Q9bnG96RnatT0XOwXSK+84R9iXXKm3Ur66aRbVUkuCCODWQUg3fukYlz07KhRUUmDesOrLV/VT+1Wc0ygFQoGFAgYQ3DoC6j92mbtNi4KnOYX7ECfmubNUumaXvlH+I5qdj2+qKhOvX+1JsisTjJLRbd2xTzOr457oz1VGcv2XuITHpI/YP41Lm5SNNqcWq5cyUp2zPSr7dYa1ey1xU2v/Yl58bNNFUnyzNB7xvoYSD0MLrW57pLMjx0OjbDOUgmvS2exVnX7Ne3yQuUHe9WyvFhFq/sUNKuUuRap84hT5ddMk+fRpSpY2GGVjssOIXN/S7KuUg5prsPAC31Jq/yYDR03rGkY89B1wHqiQXbZwxffxv58U52xpxtTLolttPwEHfOopb57aD/LQOjNDi2aFw20StTaPVqgFWEzL5Rzxm+Ylk46MMir9jsj5wXXnd9N3TOi6U++uG3sf7ojvP7D1abmWOfFfOP4MJ+sqFmNozVknrWC6ltdpOLlLerY1a3unzVpUcEidUR/gDDOGU3Lu5T35RTtPqXNrMYZ+5ljDfOGzjnu+5KH0eFhsVlVz9StumTzrSF5+D/WcMgYpo7yro8F1H3rokjbkmZV/E2p2tFKxq7C+7apPlwq0Nh3b5irml3hD64TdLbs/2D+NfbxP4UnpMknb7jXGYfxPSj+LPTB4UPhvxfmDu+44uQ5pO4fzNf8ktG/N6QWkO9A5JuCua97XsumTzgAADAeJlyopUn5qv1xtXKNy7zuFXWRX1ZN77jVa36XvKhQruh3uZBfvWuWqmlfJt+kzF4PjYvndIfD0QXI5HHBpBepZoOwyS4KHTlDX8rTuc8whz3qqLxaxat7ja+HBodxgdtnlS6JMVjNI6Gahv2yWm0zq3NZ7b34tteo2Fmquifc8SHDMeML5/pFmnNDuzx/61Lzrg0qC8/Ik+sqhwLPNqi4qEbdA8bX1B0N1kVExB/+mBBBvGN8OTf/XpQ/+q/sJyjyS7hdtpQXMmlUAUk5RKuRjMHBDi29oVO5619U/9ZW1X6jTJXrN6rWeL7AjirNmduiaeteVM89ZbL7euUxd8XXu9T7TuThpz+zHaGY0ntpDHtuS2dvCOg3O2L2YeOi1WeVFDj6x+74arAZDu5h7cZF+H9WY3W4UK7WH5RFqsW92aYbkly0R0ub+X58w7B5BYUtij36vOvrIqFMhg79tkml81rkMUvuOcq1odc4FtOqJpan2q3Gfpu0VNHYhvp0O1AIy1f1L/dow22tal44Wb5ne+VLs/Rh7uWFyjOP5fD50DjnXBZNqY1z0V2RqtRmMJOq9GXdrvAdUpe6vC/+vDjosFd9xnL2Jhn6DnwQvssHB/qSzu99tk/ehFJVmfL/bKmqdphndrNtL8dgCbWWeXM0v7pOi+bWqO+6VjWmKJGbCdv06Po1z5nD08qQ2YZYsjDaHGI+LILJ5ltD8vB/pB90RhlGatMqHGjNVV34B7IUVfGDfvlG2kaT8lT5U+M8Hw62AupdZewrcT/gjIVD08ZSrTPgkfug8df2f1VoVb+N8oU/Y23Kyxu3yH10h33yByPtzUW/NzQ8G1PSOC0Old/drJILjH2uoNY4L0zs0vQAAGC4CdtQvPfBYs1/1C8VterF9jJ9ZHX/7vj2TvWHG2w2vovuMi78zepAxoXmxr7mYVVPkvd+GG0c9WSKbXg1ncZYx3gfM2T6aZ1WPNAXCbMMjuua9dT95RrsqT/GYM9gSXtfM5jP91CVFj0S/ik4wli3W37XLOfBTtUsa1Kf+ULnVWjjU40qnBq7TM3KfcJ47Fq3gmZ1xYdqdWh1m3LvqlDfqhZ5Y3sqMz1XpxkrjK2wwFiWe5MsS7RTAGtZzRI/IcdRdYUbiU7Rm1O0YekvVmrDjcP7ejLlXlai/MGLiWij0yfSiHP0OTJtTD7S4Hj7ZdEGyKOGns+sJtOzt1Z5gS4tLWiwwhDjAr5/i8pP4XVL5sa+Xv2j9iZneLNNxfOivQ86lH/JR/K+fpbK23er2WWs2SPJ+0/98+M3atGjvpEbCp8cW801Rrg3xDrZ1v1Ozed1nkDj0DHH72BD9HZVLClU5xPG/j5iw8vR4y3GZdXa9liqrv9jjce+PoJog9kpln/4dg3JfV+Rlv4skLp3ueg5IOlzDjXY77qnX1sWRg+ImOMnU4nnxXFtBHys692jplmL5LkltjdAY+0NeOV+oUstt3fKl7RjgHEwyjYdJt2G4hNEPpcy6/wjjv/nuvGGdvkSt1/Ip87qRWqy2pZ03bPN2E+Gh+bR3hAd17XqqfVlqd9n3PMZJuWpeqv549GH8j77inU+SsPkPBUW5Mm7dpbxujGdJ6QjekwY34v2G9+Lhk5VXrUVzFd7IPbzYbTvFaPNTy16PEeOzQrp2SZVrewaCqgvKNeGnzSq5LxkJ1MAAIB4E6+kliVaDdH8Elez3a1fPWUGLbn6etlQAGCfd6+arzBuBLtU92BMEDMim6ZFf4lOZ4j5tTp1KarEYVrMl82TaNJH8u2zAi3jC3b5Q/3qX5880ErLJLuct27T/t4NVhfrUuFd9XK92675X40EWrkLNqh/txloReYPsSlvyZZIG10Op1xXlKmx1/jCP88pp7k8L7j1h5gSGZ4XIj0iui77QvhvSi+1aP6cGZpV8EPj8i7K7Iq8RjWJgxlombNf7hg+zxo6In0NfPJefkRNLxSq9juJdWkPyf/fkVu2osJIKbapeYrWNHHe0Zgy0Aq+2aWOJzxWlc/TQ9As2TGsJGPq4VAaC+/uNKtDlagkHCD+vUqWVyrfDDmqi1XXr0iVpCTDNCs1GbGh8MRj581ONazvk+/ThWr93e/UfE3MHcwLuhFKm8W25Tc0WBePZkh2UySYsy/ZqMarzImjOHwobtvmGo97cWs6gdYplKJUU7RE0xCbXLf8W3hdBJ+p0tJNI0RHSc5ngw32X1Kv5sFAy5Sk/b90h2RBv8ns5TJJW33m0Dgv8tpmT5fJ5m9Y36iyFMdrepyq/91+7YwJtEy2nFzZXjP2yxEDraB8OzrUmdgO1Glp/Ksfen9846iBlgY61fJEZP38fX7+yMGdLU8Vj+3Rxm9ZzzOzXGWXmlvlD8k/j1IN9/UZZ3kZ30Mir/ZBMP3t4/m99bn5pSvjD4t33ZEfnIzP3oRaiaeATbnXNavHs0fN11kvfrBLNcYJumqT8XmUVe1AAgCAT8KELakVNtjNvSVacsUaDRss7RDbxXtE8pJaGRrjr89Dor+GpihdFGaGNB3G/+ncJ2E5BsxG9YOquNd43CgXt6OW1IoTkv8Fn86+ItJ+kG97i/ryqlX5RetFXm7R/PUhlSwp19eduZpsVu+LzJHML7ExF1m9q2aoZpfZA+J+NZohpLE224pK1f6u2fNhz1D1vZBxEfZqt7q396r7OXd89Q57mVr3VMt/Q6na3i1U7f3l8fuBKZtKag141euzhX+xj7s4MfbnAmN/Nq9PMnq+I92qcdap12wzZu9OVWbcTtl4OoHSMpaUx6yxvy8tbJDb2Ic3qEo1z0S2XbmvQXOruxQwLvL37KhU7pGAfEcmKy8mlPZbpQti12ukBKBjeCkhi299sUof8ccfMxmXSkoiur3sFdrmbpSzb6RSSWZ7eXW6IVq92NhjCu/dq40LMrl67VODcR7qGnOJoVGkWaopcZ0E99ap2HjfQWMLlD+WEBomlNYcFN0HzFKLfVtUPt6980UdCyl4+EN9NGlyuLHuZNLZ1maVvQ+PnaXJU0eqAp2BY0H1rpmrmje+qZ6t1cpLvmgK7arRrFW94eAvfExY09N2SktqWSMnYtjnmtkW2VJ1XfWYNljhYzxjPa662vhsMj5oosdhmtsn+HKfsV4Krc/cwPCSWtHPomSf6VZJLVt0/563QW+ss3r7HVG0NJZTjX3bVBGz3we2L1XBGrdsS7Zp/53RVztVJbXi943gy22qWtIeqRptcpSoeWuryim1BQAAUpiwJbXCvtiojUuGLjedN31zeJBxSa1aw/fxq/3fTudGtFOULgoPZliV7n0S5JSr9aHRA63M2ZRrBVqmvIX1Q4GWGVrc1SHvC9069A/GfWIDLVPChcHsK1zG/yF17bbarHmzW93vGn+NL7uFMe2b9K6eo9Ibm9S+KybQ+mKtdvbv1xsvtqrM7pfPbE/kH10qi2kIfnC4Jl/hto5yXcPnWcNQoPUJy8lXSWKgZQh4PVZwUahCc7Wl6xynCq81nvO2RpV/ooFWLJvyr4vpcj6NYbC7+hQ8m8w2qWyqWBDfe5m9yOzwYFu41Ip5gRXYsUKlBbM0Z01fivZdAupaPkuzCorUtDdVCzB+9T0XOaOUXZfJJV8azilTxRKX6p8a+UI6+HKn6ubOGmovLyxXrsszCbQiIu8yX3lWoGVemCZtoyitIdJo/jA2Yx/MYLuaPbBGzvFBda1qUN9ohVbMXuxuiVTFdd2zLj7QOhJUaDxLhUR7tksRaKXLFu6dc7wCLbONqGI1DHzf2NdTB1om26WFKrmkRPVryjMPtE65zHq0jRvuLBuq1h7HrsJ7d6YItAwvt6nBDLTMQHVdfdqBlsn+xWigZXIY57mEz5roZ5F5rCbOi573P58X+T7jje8QIaU3e9VtngSM5Qx9GHvOCug/jM9MU8lVyX/MOZXsxmf2Nk+PGq+x1nugVw0lV6vqmdP32xkAAPhkTexQy+BcXDkYZIWOHbVuxXN+rzHyK+ObLWralX5R/lNrpC/t0V9y07nPJ8GtltgGlQtKrVI4QXWvjJk+OFSp0wytLI6iMoVjrSc71X1E8vV0h8NH29xC4xJ7yDTz4tGer7LbNmjn3dYv17n5yo+Wtol28X9i15hJ+NRSkuzCPZ3hxEokxQvp+d9awd9FLjkT2ogbWa7KHzIu4JY5U5Y6OvVyVbZyqMv5dIb6eSNcfh9sV4NZVeiiWlWGS/zFs1/mjFTDOubRIz82I2Cbyual6rrfoX+51jyiQup+sDN5GP5Or7rMbWurUHmyTOtYUIcSqk/GVaEMHho2L7YTCdedSRqttoQO9qplcYHmLG5StxnkmtWL19crnfIcSUWPnWRsjiRVqFMMSRoPj/OPZarPcLs679ioinCu1a2qFV0x4V2ioPrusHqxO69cZcd+roY1VZpvnHPm5BvHonOpOgeMeWYJmKTH6kjDUEhnlhxKfp/hQ7i0isH3QHHS+UmH25PGgaML+dSxuEg/nNSo3Y+Vj96GlrGONuzYoMqEDkNOTyl6tE1nGAyQMnDMp/a7IqXA7de3qj62hKAlcMAXnn/SnJcX+fw76Euvs4QLKtW4xDiOwp0EFA31wjjwG3WZVXFTnac+CeGqmv3qX1diBY7G9r309I9WAQDAJ2OCh1pBdT8Q7SZc8t5Xow7zAi+RvUy1385T3sJm1Racbl/gp8lllVZIWrooPLiMr3ymkb7Yl1mlHlzGM36CPv7QuDA3L5AjPXGdFZk66MNwD1eH4kvHOIxlv9a80av2n7SpzazOZbzXyhviYzrnbS/qjRd3qnVZifL/T5KLZ78/0mNiXp5yzep7iW337PMq3HKP3z183rPeES6WTxdeeazu7h1F0X0CUZ6nOoxzgU1lKytGXDeh3R2RHgVThF9RjgW1kTDlTWOfTFJay/9cV/jcY/taiVzJAoSD7Vpk9iaaMJiN0Zt8jy4aNq+goMlq8H8EZmmk7xnnupcje2zewg3a4+lR83XDS/YllewCOXrs2OzDQs+87z2VtFfDpMPWhOrf42GSU40/qQgvV7hUlLX8/oMJDeAdc6v7GStmeKdLDXe3q2t7n7zGOcesvujIcSk/tpTiJHNakmAuYUhcp+GSVUnuFzek2BA2s2RXsvvHDmMp+RX0qG1xqbqv2KLd68vkyKBUUXY4Sb0fpuB/ok5tZmBtVmu/M7GTgoB6bzeO1a/OV1O418STZFK+8i8yb/TK/Wp4ysiM/bnwzj3hoMhuLuOqAhWv7VP3wy3hUtz2hWXJz1OfIMe8DcZ5Y4OaH08d4AMAAEzoNrWCz9Xp6hXdCtkKVX6NR13mF8wMeno6PdrUSsfY27bIRGZtaiUX7Skq/4492vmthHVqXOSXzm2T77JG9T9VEV8lJLF9tMTeEBMla0/H6jExb2WPei5qj8xPW7J1+wm2qSWzzTLjgjyUJ9c1eZGLqlHa0/KsL1XL327QtmUxsYLZY+XWJjX9MqjC79yr2mtTrtGTJ9Cn9vW9MaWdgvI+02u8N7P6YZnyE1OUEQRf61bvgZBsM0tUdmnMA6eX6PvXenTjPbl63NpvIvtz4raLttcmlazbrw3zhgIEf5I2tQJWr6rD2+uLPk9sW3CW6DnBrGp3fX7CBfEI7yHMqYp7yuNKKIZF9/doGzUHO1T3E+O176iQc7BThuh5YpT91Xyuep8Kv1mre28tjBxj+xo0Y3lXXPtK0fWR0fkx1flwlPaXRn+tkALvhOQ4b2h9RR8zdA4wq95dr/bDV8o5M18uZ77yL79Q0xJ7rEx27kgpevxmcO492KWlNzTIHTS2f9H/qHdvQI5L8vXR616poFnb0ilFlYmgW03lS+VZEN8D4qCBLtXc5FPFL+vlMmce9qjzviZ1fVio6rtqVTLWKtejbNNhxvhZGTmOR2pPchTR9qvS/VwzlnO+sZxe48gta99jnAuGn6CG2nrLV/3unZkHMmmuC89YekA0BF9tU9XimHarkrYv98m0qQUAAJCpiVtSK9irplXd4RI/hfe2qtkYyszvnq+3qO6nmf4ue+oF9rWrYU1DmkNMm1pJ5ycf2ved4rJHLzepyqz+NalMtQsTvsaapUtuM0vV2VVxe0KgZfpirRoHQxrjPreOEGil4H3t+fBf58yh6CHv29vU398/wrBtqCH6YXJVscPs+SwhIDgSUCDJD/Shg271vZk4I1eVz+xXz+7+DAIt472sn6/iG2tUs7xULfsi0/zuaLtJLrlmhW8MMUupbPYN9pgVETQu8ueq45xqPXajTe0rboiUPhhFKGhVhQuOUCUtE3/1qXt7l7oGBzPQMmeE5H02dvrogxkGhR95oDd+Xo9PRy/6ph6/f+T9JrSrLRxomSFVbUyglYpjca0qzLu92662cPs6lmi7b7ZylVwemTTMKFXtcufVD5vXnCzQSuaCSrXeHxtoZSho9kJolVw0+A5YLfLNyDtNL0BtcYFWrLy86BI7VLa+Xz2PG58Hd1Sq7DqX8sxG/kffzOMmuK9JxXPNQMs4dzy+RfVfjFR8+/u5rdrzUJnU36DSuU3qOxyefOKCfWqYu1SehT3DekCMCKrvwRb12nN1oTkz2K2ar3bItvwxVZ7VrprFQyWdx40ZpL86fu0jue40z9MbVRsukWxTr9mG5EP+4e1UxQ0x91vcrN3muf7ONBohND+nVpqBVqTaYWOSQMtkttHXusCc51XL99rTqx44Bs6rIhWKfc9ZvRjHcau9uk2dL/iGtRVnv6xW23bXD51LjG0fOmLdBgAAyDITM9Qyv3jeVKNu8xrzimbdO8/4cmkvVOO9ZeEv9d4HatSerBriaeToH7vjL8pHHNxWKRe/3EnnJx+6/5i8jbGTJTgpL9Kw+7FuVTlnaf6aLnnNizezNy6rrRv7glbjQi9893hBj/pesm4bF2IeT8YVRuTea37td8kZmwrYpw117Z50mCZ7qlIT5i/2s2aodM3QBUVwV41mOAtUsDoSqEaFnqvRLOPisurbZhW4GCG3mq6ZZVzILlfHO9a0UXnVuz36LDbZ/tb861PX1ui0aZqWkNz4NjWp86J61ccGNS+0qc2xThsW5OnoIfOxfh16LzIrNb86F0eqwq145kNr2gnKrdC2YWFi7LBH2x4fIXzcvVEbHt+TfF50eMKscmhsz5FKnRzzqv3BSJf3JSur06smN8mlipsjoUnvT4ba1jKrOpq3U1Y9PI0NVtubNk2RyCVgHDuRfavwqkx6H/hkDV7ITxoe5SQTCvrlG4g9asdRyK/e1cWas9zYRyY5Vb+rR41XxAYiNtmvbdVus2rYO52qchWr4Vl/3DkkY+HPwSp1GScn/xM3qsA4/9St71Tf6/5wKO17oVMNc+eES99Gf0hwP9imafdvUPlFRxU+Jbx7aHCfHg/BVztUdc0cLboztmRmpszeAoeqhvf9/hW98pJbfeb4E90K/3RhOyRPzH2GD9Zn5mGv+p7rk/sl4zl+3xczP3l1c+/6qkibbPZytd6dWO0wlk2F91htvb3ZpkUpO5zIgNmbZsAv775edW/qjlQHdhVG2skz1uvPE3+QMO7r3tuuphvb5U48B5kdBjzQHnkOk/k5NLdYTfuS/BqTBUKvt2vpnDmas6BF7ux8CwAA4ARMwFArpjFge5k2PjRUMsN+baOaw6VhfGo7ib+ejofcxaOVIIodWq0GoEvUmnR+8mHb4lNb5sJ+WYVad7+hF7c2quyCkLzbGzTfNUNzXHNUY7Z1c0m9tt2TpGFusz0Y4+KsO+bLqveBRarbm8G31ze71Gl+6b+oUK5Mi3glFVDXGrN6iHTI2I8mRybKblxkhC/7n+uK9DRlsV1bq2qzvZ53O9RpNsobZXOpbK71i/6akRq5jmWXPRzO5KpsfY/qvxiS79E6tX/olDPcJpBHHnP/N5mlItYvUul6u+ofiK/m4d7nVXmFubRWIGarUFlBZF5KRzxyhy+e8lQS7Z3qREV7iUsynPXOb/TDJcVadGOdfj6Q7D7Sf9xdpZobi1WwYJU2v/SBJod7i0sY0miHKLCjJVJKKzH8G0Xe8trI8WdcvHaY29YsFbfdvIS1qbwse0KgKN/BSIBluzgvcu588+fqCLfZU6iyovTXyyfN74u8j6TMgOBdr/qebFND9XwVzJqhWXOK1TIYnI+XkPzPNqjUWayaHf5wCbqN/dtUeVHy9Wi2IbTnqWo5J/nVdWuxZhXWqG3fWMIt63Pwv12qfqxHuzd9X7ODxn75SJOqFhjHSkFBuKfYroNS/m3b1Bj+IcGtvj+Wq8KsKmudL23fKMuoWllKZqi3plRzbmhRn3mSmz5NZ0fmjMEfUvfya1YjNO/yeofqks0fHKzSzYFuNaWY/wdzfgyzOYOlj5pRWJ5qn2pW4WgdccS09RbcUaeGdD6vQkEFzODqhWgpyW7VmZ2nGPvnjPxZmlNQrPnLa1T3QJ8OmbPNXlC/Ye5LfnU8lhCcveONvMeL8uMD+vCPSHNVZzbHYH5HcveoOdyWqF+dy+do0XqPsffESlUCPEWPyqdcQN33t8kdDCpobPeWp8czhgUAANlggoVaQbnXzldVuDFgu8paGlUY91OqMe2uZrkmOVR4Q4lyRylBEf2lf5p97F+/9WFwsApPRs6xD78wTznYrSAodTiQbLBn1Dve+LF/0Qy39mvnbZEWUIzvomG2Yz55/pjwxT8caC1SuxVSbnC/qI3Xhy8T1F09d6gHp1G4OyMlZ5w3fGVcqk8Fd61WQzicKlTjv8UEcWaj9uHg1K2u3bHLlqey681XDqnzifhSXIO9b77QoNVp9b6Zq4rVZkDlV/ftN6hoziyVPp6r1u5t2ra1VSUOv9oXzNAcs1e3S42LlGfztGGvcSGdUI3SZVzMVpsNDVtV5WzpNBR8wBNpqPyicpWMqQ2xdIQUeKFTdXON9xDtvc9m16H/SbKtj3wg20xXpOHrAbdxsVuqWZcWaOl9XfKGOyRIU8itHz1gvjO7Kn6QvI2XxCo8gwYvLI1tu6lbh3Z3qjOcaZWrLFmpwyizlEhc6ZHI0Hcgcsb44EBsyZHYYaydFtitqnY+eVPlPceM7Wt1rue8+HPG/yH1PWaVOru+QiXJzhlJe2lMMRweZf9+s0XFSRrzDre/kxHjXGIFVLk5ka4xAq93qi3c2+GcSEBQZHxWmI3F7zXWZ8gmxyWFKskbPJJPUHQfnqXiW7vCP6A4v7tNL+6qV+EoVULDVcP6t6neDI0HetW+vFizZhnLur5T7jeHer8cSWhfi+qecap1zxbVXpMnh3G8bthtHO+XWXcwhXvE3BPTxp5L9VsjJRQjPcyeYCgb7THzvzfrBjPUM0uXWq+5/7GywR+bMleo1jfMat+Jw4tWFW6zHbtk82OHjSoz72q26ZR0fkwbUdYPA3NWmG1kGQqM7w5/TDgmzXA0IfSpWlCggpW/sbaX8XlV26TeYbu/T53LS8O9/s4y9/VZc1RgBldrrHDOEDQ7T7FWpT3HobwrjM+YbxUOBlWumyJt+YWeiQ/O/K95wq9tczmHzmch8/WMbRH+jpSv+qeM9znV2CaP7dGGBZEt4nlkkYrXuGP2s1QlwKMlxD9pDjkLomvDLie9JAIAcMaZQA3Fh+ReW6SlT5hfBe1y3bNNWxZGv+gkMC9OYy/ezZ7wXg0q7/ILreo2R/U/fW2qWR35Yl+xdb/1S/ZIzCoRr+hQbr6+kGOFYKH/0W/uWxrpAemCWvXsTrNKU8ayp6F480LH19+p9vtaImGFwbmwUva9HZFf8E0z69Xzy0rlDXSrZkGdes3p4So7VjBjVqtZWBopjWfIXbJRO+8ojKsmGNxRpTmrjavzBcayfttnNexbolbPBpWZF+bRxqDTFrNug72quzpSvTVZg/ehXVWatcp47cRGcAc6taiwSR451di3TRUxVeEGG861V2ibu1HO0cIlk/mr/n979ZZxceK8wC7b4GNCCr7pkdt4urwrnMobpZSS++5ZWvrkNFXv2qPacG9aqfkeLVXpgz7lfrdHe24d5705ZBxDuzar6cEOeaL7gnERXHZHsxoXO1NXAzWF96t2ta1pV++ANc3guKJSd91dq5ILhq+DYQ3Fh/zq++0hOa8zXstYt2aPeIPtLYU8apm7SB3vpmjQ/x23ej+8UIWXTJbnbuM89GRAtiXbtP/OJE1XDzYCPVZJjvPEhuKtyfFC6l4xS3XPGTfNqsALnMN6Qj30Upf6wseltY8G2lS8oN04D+YO2z8G99kxSXgPg+vE7BU1Wu4xxuHIhX18g9hWZwnGWXV2XuTMHfGBXnmwKlIyKma5A9uXqsC4WA+zOZRf8BWVXVeowoJ85Q7+MGDI+Nxgst7PsaC8v2xXy4875I7uhxeUa8NPGlVy3vB9cLQG8IMvd6jue1bppqicSm3rrZdzhMM6+KaxXs5yyTXsNUMKvO7WH/QFuWY6Ys4ZMY651eRcqk5HYucHmUncPxzX1Ouh+ytTt/OWZuPoSRnHbtftN6jhWWNFxXRmkJr1mTni8RIx2BnEeChq1YvtZcaZJco4JquNY3KvNWqyO+T4xy/oX/LzjX00X3l5s3Xh/5ksxwiNv0U7YDGDquqntqj2so/UeUOBml6VCtft18Z5NoXe6VLd4obI5+ng/WJ/9QvK8+BSLdpksz5ro98rzBLgd+lK615DntcPCozP5zFsr/FvKN78zPPr0P/JVd6pbCQPAACcFiZW74dWD3l5d/Ro27cy+Coe7XXPGo1zbrV2Pler/FFDBp/a55aqLUVbXXbjAvfFZBe44+I0D7WOBOV7rU99u7q0eYdbAavEi+OLlbrrvlrrYs+sptOkqpVdyr3/Rd1ra9ENK7oivwTbXWo2voCXx5Y0OuZT1/JFaui3fpl2lOuBKr9uu9e6aLWY4WbhU5HqqLnf3qk9K60GtawL1+Q9zMWK9sQXXbdmCbE5kYuQVD1pBrq0tKBBbuNyML6XuYC6biwIl/By3t2vbd+IKasQvZAcduF+kiVewA545D6SL1eSEGho+YcHHCfksEcd9zWp7RmvsRdYwmFWo/5toUuODK9Rgge61HJHk7qsRuPDkvSWmbz3Q8u7HZpfFGkQOp6xL/Yb++IoxUxCAbe8f3XJmaw0W/Ti3VGmxjUlw4Kl0eVq9nX58SVd0gq1DAc7tGheS0yvZ8k5w+dQmzoWFFtt3W3U7+6NrxocvTAd/RiKEfSq+1lzOyecq6LrJMXypwp/+lYb23CHNZKE3ThX7THOVeGlG3Cryzz2rhkl6I2uy0l2OaadZU1M7cOBQNz78W2ar9IHjD3HUajq+xpVfU1u3HqLNVqoFWGW+urSjx5+xFj+z46tN71MvNCkWTd2apoVXAdedeuDi13Ky6hkr1+dNxSHQ5VI6azH1Txaz6qZhlrG54r/oFu9j7cPnTuMz4GNu5sTSmgnk36oZZZebHItUqf5UWMGTuZ6cJihk3XkOvJVOCN6FNtjfhwznBUJo0L7GnT18i7j08MMmV7URrOdT4sZQLr/P+OYzpumybHhaiYSfuixGU8SCq+QyPnqX16q0dxbe8Ovn/TzNIb5OPPxo3+vGPv3jvEPtQAAwBnNDLUmkrf++JZ1KxPPH//hNVcdv8ocLr34+MUXXxq+/dXbfn78lQ+su6ThP24zHxs/XHrV3OM33vub44c+tu50UvzH8VXh11tl3Dp5Bt/fbRm+yns/P37D4Dq51Fivm4//h++oNTOBuZ7++h/HV19u3X/hj1Jvg48/OP5K2w3h+839ibHd+1ZbrxEZrlr2i+NvGc/31mNfPX7x5auP/8dfrceZ9qyKPO6xP1sTUvnz8c1fNp9vaN0e6l19vGjmV49v9lkThjl0/Df3//D4z91vHT+asN2P+l45/odDyd/7oceN93Ld6uO/SbVuToKj3SvC6+GGxw+Fx5+/q+j4D9zhm0kY28Vct1f96PgfrCnj49DxXyyxtts1Nx7/4e7h620sjvp+c/yH37gq/LzLu4fvRJH9ee7xzX+yJsSx3mvscPlXj//QncEJIZU/bT4+13y+L2829q5xYu3PaT3n0Q+Ov+X+zfHf7E42PH/8rQ+G9r+jxjF1+cXLj/8qydv+82Nzw685+jEUI/reE89Vo6yTP29dHj4nL98aPzc6/aprLh/aTsZw+TVfPb7q8VeOfzCW/Si6LtM6zw0/Pxw/bqzf1/58PJ2jOON1OA7HxciOHv/VLeb7ueH4z98zRj9+/vgPvvSD48+P5XV9m4+vuGVz+p+hqfaNFCL7ZmR7XzzzquMr2p7P4HPW+sxM8xg8+sEHJ3hO+uD4b1Zeaizn3OOreyPn2nH38aHjv/pe5HwXHS6/4z8i++EHxvs1PlMvvymD7WGcl/8QPif8wbiVzNHjHxw6dPzQIWPdWFPSFd3vx/UcCAAAzlgTq6TWGStS9dGsIjSsBMc4Crzeq1fCNXpmq+SSTF4lJM/P2uWdWaayy/LS60I/6FbnMzaVjVbtzBA84NVHFxvvO+X9ggoetsseW+3FrLr34Uc665zR2xYLHQ7ow2Nnxf+KnliFdTycjOccReQXc6l2d4+q1aFFa+16qCO+RNOgQJ/a1/cqeEW16ueN82/rB93qk1OFSUuInZjQO359eJ7Z92G8UNDYrh8Z23VqbNXNU+CIT+59PgUn56mwIG9sJTMSWfuzJk1Oq1H8TAQPB41jZ3jRl5C5zf5ozLu4MEXJviSi7/0kn6tOSLg6unGiS+s8Z1Z77ZPvw7G9nzGtw5PKr455xWo5Fqkur02L1GR/SFsWnoItlfG+YXyubO/UofyvqDBVVcqUrM/M8TwGR2P2rnksV3mjtKl2okJBnzy/9ylk7L8uY/+NvrdgICCbY2j8E3XEOF8dOTnnKwAAcOYh1ALOZO90aH5Jm3SdSzowTbVd6VTdATBR+X82X8UPSiUFkndqrXbea1XfBAAAAE5DhFrAmS5cykenvsQSgNNSuBTjMUrRAAAA4PRHqAUAAAAAAICs8zfWXwAAAAAAACBrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQagEAAAAAACDrEGoBAAAAAAAg6xBqAQAAAAAAIOsQap0hQu/0qm1NlZbeWKe2Z/0KGdOCL3eq4cb5WrqmU55g5H6nUmBvg5au6JLfGh804FHvPp8+gUUCAAAAAABZglDrDBDcW6f5P/DL9Z17te6+QvlvL9ai5Uu16Kc2Vd7fKOdrTVq0ujscdJ06Pv3igS65Pf6E8CqgrtsXqWZ5hzzWFAAAAAAAgESEWhNdsFt1D+Sq9bFKuc51yHGuU/n/KHn32VR5b7lszzap/YB5v5A+jDzi1Ah45D5o/C0wlicyJeLI8+p9wfhb5JIrMgUAAAAAAGAYQq0JLvBst2zf+abyJ1kTjnnlfdP4e0WJ/sUu5V5TqcqFlWq9u1yOyD0i3unUojlV6g5Y4+Ms5O6V2/jruvwLkQlRBzzh6flXOGWLTAEAAAAAABjmU8cN1m2cCV5u0qzFnZr87Z3qXxlXRireO91q+aVdN99aGBd2ee6bo0U/y7C1q+s3av/9hXEhVd/qGarakaf63h5VnmdNNHgfLND8R6XqHf2qvcSaCAAAAAAAkIBQ6wzj31Sq4gf8qti6X41ftCaecl61FcxX+4cV2ra/UU5rarg9rRsL1PBCmTZ6W1UYLV0GAAAAAACQgOqHZ4BQMNoEfEDufT7jr0vOmZEpYQMeeQas2wrJ/2ybGu47iT0ivutWn1mt8VpjOSJTIqLtaV3h0hcmScE33fLRBSIAAAAAAEiCUGuC86ydo1lzZmnpjsBQaHSJS85zIvPNEKt7bYP6rFbig7ua1H5Wpco/3aa6n5oB2PgLedzyGn/zZiZUf3wt0p5WXoFTDgXUe0+7PB9FZgEAAAAAAMQi1JrQ/PK4zaJOuXLOcCiwo1N95uTPTtM0868h+FyD2s+pV+VF4TH1+fJVXXRIfbuDys/LDd9nvLl/H14K+V72KFqGTId7VbeiMzyef1GeNPAbddvLVRbXej0AAAAAAEAEbWpNcL5N8zV/xzSV5QflOVyuDXcE1XB9i4KF5cr/q0fenGo9fl+ZHLHtV5mNyd8kbfQ0yjXu7VpZ7WlZvSrmFZXLeY5Pbl+e6teXy7t8kdpVqMJJQbke3qbKCyL3AwAAAAAAiDXxQ63DXnU93in3f4eUe22tqq/LHeqF75hPXbc26NB3tql6Ave0FwoG9OGxyXJMtd75sZCChz+UznHIPlgNcYj77lmq0kbtvyNfQdllj+228ES926H5RS3yXr9Rb9zjVODDj6RJMcsmY9kCqZcNAAAAAADANLGrHx7s0KKC+Wp4pEvdu7rVfmuxilb3KngsMtu3qUYN75WoJLbR9AnIZnfEhEaGSTbZHalCoz51PzlZleUuBba3q/uwNXmcRNvTcl3+BXPB5DCWI27ZNNKyAQAAAAAAREzcklpH+tRQWKUus0kpM9RRQIFoT3o5LpXlH1L3Xpvqd+8c3ypuQZ/6fu8baivqRJ2TK1dBvuzjXg0wFY9a5jQotLJQoVC5mr+VZ00fH32rZ6hqR57qe3tUeZ41EQAAAAAAIEMTNtQKbF+qggemaUNvq0qmWhNDQfledav3Z3Vq22tTWfsetRbZrZkxjgXk/umvFJxXqZIca1qaQvuaNH/Nb/SBNX7iZuvfdmw4tQ2mm9UT/yrZx7XeoclqT+vDCm3b3yinNRUAAAAAACBTE7ekViiYvD2og11aekOLprWkCLQMvvXFKn3ELy3YqDfuLbSm4sT51bWiRn1f2qANC05Oz4oAAAAAAODMcEb1fhh6s0NLr29X7vo9ar02eaBlCr3cokX3BlX9cHPGJbUAAAAAAABw8p0xoVbw1TYtXdwt5093qvGK1IHWRDJjxgzrVvZ64403rFsAAAAAAABDzohQK/hcg+au8unrj29U7WVnRqAFAAAAAAAwkU34UCuwq0ZzfyA1dm9Q2ShVCUPv9KptTbu855Wr9rYKOceSf4X8cv/Wq2hHiyfslPd+CAAAAAAAcPqb0KGWb/tSLXpgmppje0CM4XmgVO25G7XxG7nS620qvUdqfLBQ7mWL1J7Xqv0PlSnT/v8mRO+HAAAAAAAAp7kJG2r5frZIpQ/nauOeVhUmK3F1sEPz57bJ+fh+NV4RUOcNK6T121QxrU91+VXqvqRee3ZUij76AAAAAAAATj9/Y/2dUIJ767ToPo8Uel6rywo0v7pFHc+45QsEFHjXq971VSq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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "70cd6012",
   "metadata": {},
   "source": [
    "### 1.3.1 归一化与标准化的区别\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "**归一化**: 将数据缩放到[0,1]区间  \n",
    "**标准化**: 将数据转换为均值为0,标准差为1的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cf606c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.031798\n",
       "1    0.086221\n",
       "2    0.058771\n",
       "3    0.064145\n",
       "4    0.061260\n",
       "Name: Annual Income, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 手动实现归一化\n",
    "# 对Annual Income列做归一化,手动构建函数实现\n",
    "# 自行学习下如何创建函数,这个很简单很常用\n",
    "def manual_normalize(data):\n",
    "    \"\"\"\n",
    "    此函数用于对输入的数据进行归一化处理\n",
    "    :param data: 输入的一维数据(如 Pandas 的 Series)\n",
    "    :return: 归一化后的数据\n",
    "    \"\"\"\n",
    "    min_val = data.min()\n",
    "    max_val = data.max()\n",
    "    normalized_data = (data - min_val) / (max_val - min_val)\n",
    "    return normalized_data\n",
    "data['Annual Income'] = manual_normalize(data['Annual Income'])\n",
    "data['Annual Income'].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1175e6d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.031798\n",
       "1    0.086221\n",
       "2    0.058771\n",
       "3    0.064145\n",
       "4    0.061260\n",
       "Name: Annual Income, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用sklearn进行归一化处理\n",
    "# 借助sklearn库进行归一化处理\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "data = pd.read_csv(\"E:\\study\\PythonStudy\\python60-days-challenge-master\\data.csv\")# 重新读取数据\n",
    "\n",
    "\n",
    "# 归一化处理\n",
    "min_max_scaler = MinMaxScaler() # 实例化 MinMaxScaler类,之前课上也说了如果采取这种导入函数的方式,不需要申明库名\n",
    "data['Annual Income'] = min_max_scaler.fit_transform(data[['Annual Income']])\n",
    "\n",
    "data['Annual Income'].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d21cc1e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1.046183\n",
       "1   -0.403310\n",
       "2   -0.727556\n",
       "3   -0.664078\n",
       "4   -0.698155\n",
       "Name: Annual Income, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用sklearn进行标准化处理\n",
    "# 标准化处理\n",
    "data = pd.read_csv(\"E:\\study\\PythonStudy\\python60-days-challenge-master\\data.csv\")# 重新读取数据\n",
    "scaler = StandardScaler() # 实例化 StandardScaler\n",
    "data['Annual Income'] = scaler.fit_transform(data[['Annual Income']])\n",
    "data['Annual Income'].head()\n",
    "\n",
    "    "
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "vs",
   "language": "python",
   "name": "python3"
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  "language_info": {
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